Post by rmarks1 on Nov 23, 2017 18:55:20 GMT -5
A bigger question is how the computer is coming to its conclusions?
www.nytimes.com/2017/11/21/magazine/can-ai-be-taught-to-explain-itself.html
Bob
InSeptember, Michal Kosinski published a study that he feared might end his career. The Economist broke the news first, giving it a self-consciously anodyne title: “Advances in A.I. Are Used to Spot Signs of Sexuality.” But the headlines quickly grew more alarmed. By the next day, the Human Rights Campaign and Glaad, formerly known as the Gay and Lesbian Alliance Against Defamation, had labeled Kosinski’s work “dangerous” and “junk science.” (They claimed it had not been peer reviewed, though it had.) In the next week, the tech-news site The Verge had run an article that, while carefully reported, was nonetheless topped with a scorching headline: “The Invention of A.I. ‘Gaydar’ Could Be the Start of Something Much Worse.”
Kosinski has made a career of warning others about the uses and potential abuses of data. Four years ago, he was pursuing a Ph.D. in psychology, hoping to create better tests for signature personality traits like introversion or openness to change. But he and a collaborator soon realized that Facebook might render personality tests superfluous: Instead of asking if someone liked poetry, you could just see if they “liked” Poetry Magazine. In 2014, they published a study showing that if given 200 of a user’s likes, they could predict that person’s personality-test answers better than their own romantic partner could...
Kosinski first mined 200,000 publicly posted dating profiles, complete with pictures and information ranging from personality to political views. Then he poured that data into an open-source facial-recognition algorithm — a so-called deep neural network, built by researchers at Oxford University — and asked it to find correlations between people’s faces and the information in their profiles. The algorithm failed to turn up much, until, on a lark, Kosinski turned its attention to sexual orientation. The results almost defied belief. In previous research, the best any human had done at guessing sexual orientation from a profile picture was about 60 percent — slightly better than a coin flip. Given five pictures of a man, the deep neural net could predict his sexuality with as much as 91 percent accuracy. For women, that figure was lower but still remarkable: 83 percent.
Much like his earlier work, Kosinski’s findings raised questions about privacy and the potential for discrimination in the digital age, suggesting scenarios in which better programs and data sets might be able to deduce anything from political leanings to criminality. But there was another question at the heart of Kosinski’s paper, a genuine mystery that went almost ignored amid all the media response: How was the computer doing what it did? What was it seeing that humans could not?
Kosinski has made a career of warning others about the uses and potential abuses of data. Four years ago, he was pursuing a Ph.D. in psychology, hoping to create better tests for signature personality traits like introversion or openness to change. But he and a collaborator soon realized that Facebook might render personality tests superfluous: Instead of asking if someone liked poetry, you could just see if they “liked” Poetry Magazine. In 2014, they published a study showing that if given 200 of a user’s likes, they could predict that person’s personality-test answers better than their own romantic partner could...
Kosinski first mined 200,000 publicly posted dating profiles, complete with pictures and information ranging from personality to political views. Then he poured that data into an open-source facial-recognition algorithm — a so-called deep neural network, built by researchers at Oxford University — and asked it to find correlations between people’s faces and the information in their profiles. The algorithm failed to turn up much, until, on a lark, Kosinski turned its attention to sexual orientation. The results almost defied belief. In previous research, the best any human had done at guessing sexual orientation from a profile picture was about 60 percent — slightly better than a coin flip. Given five pictures of a man, the deep neural net could predict his sexuality with as much as 91 percent accuracy. For women, that figure was lower but still remarkable: 83 percent.
Much like his earlier work, Kosinski’s findings raised questions about privacy and the potential for discrimination in the digital age, suggesting scenarios in which better programs and data sets might be able to deduce anything from political leanings to criminality. But there was another question at the heart of Kosinski’s paper, a genuine mystery that went almost ignored amid all the media response: How was the computer doing what it did? What was it seeing that humans could not?
www.nytimes.com/2017/11/21/magazine/can-ai-be-taught-to-explain-itself.html
Bob
Shouldn't there be a provision as to how Facebook, dating websites, etc., members' information would be allowed to be used? I will never use Facebook or any other website that needs personal information to participate.